Synplex: A synthetic simulator of highly multiplexed histological images
This provides a valuable tool for researchers in bioimage analysis to train and validate algorithms without manual annotation, though it is incremental as it builds on existing simulation methods for histological images.
The authors tackled the problem of lacking annotated datasets for multiplex tissue immunostaining images by developing Synplex, a simulation system that generates synthetic multiplex immunostained tissue images based on user-defined parameters, resulting in a publicly available tool demonstrated to simulate real disease paradigms.
Multiplex tissue immunostaining is a technology of growing relevance as it can capture in situ the complex interactions existing between the elements of the tumor microenvironment. The existence and availability of large, annotated image datasets is key for the objective development and benchmarking of bioimage analysis algorithms. Manual annotation of multiplex images, is however, laborious, often impracticable. In this paper, we present Synplex, a simulation system able to generate multiplex immunostained in situ tissue images based on user-defined parameters. This includes the specification of structural attributes, such as the number of cell phenotypes, the number and level of expression of cellular markers, or the cell morphology. Synplex consists of three sequential modules, each being responsible for a separate task: modeling of cellular neighborhoods, modeling of cell phenotypes, and synthesis of realistic cell/tissue textures. Synplex flexibility and accuracy are demonstrated qualitatively and quantitatively by generating synthetic tissues that simulate disease paradigms found in the real scenarios. Synplex is publicly available for scientific purposes, and we believe it will become a valuable tool for the training and/or validation of multiplex image analysis algorithms.